This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …

Data mining (DM) consists in analysing a set of observations to find unsuspected
relationships and then summarising the data in new ways that are both understandable and …

In this paper, feature weighting is used to develop an effective computer-aided diagnosis
system for breast cancer. Feature weighting is employed because it boosts the classification …

Modern machine and deep learning methods require large datasets to achieve reliable and
robust results. This requirement is often difficult to meet in the medical field, due to data …

Breast cancer is the leading cause of high fatality among women population. Identification of
the benign and malignant tumor at correct time plays a critical role in the diagnosis of breast …

The purpose of this study is to develop a hybrid algorithm for feature selection and
classification of masses in digital mammograms based on the Crow search algorithm (CSA) …

Due to the complexity of medical images, traditional medical image classification methods
have been unable to meet the actual application needs. In recent years, the rapid …

Clinical decision support systems (CDSS) have been designed, implemented, and validated
to help clinicians and practitioners for decision-making about diagnosing some diseases …

The lack of interpretability often makes black-box models challenging to be applied in many
practical domains. For this reason, the current work, from the black-box model input port …

Histopathology refers to the diagnosis of tissue diseases and involves the thorough
examination of tissues and cells under a microscope. Tissues are collected by biopsy and …